Objective To determine the knee joint cartilage thickness using different methods and explore the feasibility of mathematical statistical models of dataset for the prediction of cartilage thickness.Methods A total of 304 patients diagnosed as knee osteoarthritis(OA)combined with varus deformity and undergoing unilateral total knee arthroplasty at the Second Affiliated Hospital of Army Medical University from March 2023 to March 2024 were selected for the study.All patients had complete preoperative and postoperative clinical data.The healthy cartilage at four anatomical sites of patients,including the distal femur lateral condyle,lateral tibial plateau,posterior medial femoral condyle,and posterior lateral femoral condyle were selected,and the knee joint cartilage thickness was determined based on preoperative MRI analysis,robotic navigation system tracing,tissue section of surgical specimen and digital vernier caliper.The baseline indicators of demographics,disease and imaging ffor patients were collected to construct a dataset,and four models of linear regression analysis,principal component analysis,Least Absolute Shrinkage and Selection Operator(LASSO)regression analysis,and K-nearest neighbors(KNN)analysis were established for predicting the accuracy,determination coefficient(R2)and root mean square error(RMSE),and the regression equation for predicting cartilage thickness was established.Results The knee joint cartilage thicknesses determined by preoperative MRI analysis,robotic navigation system tracing,tissue section of surgical specimen had no statistically significant difference with that by digital vernier caliper(P>0.05).The predictive efficiencies of models of linear regression analysis,principal component analysis,and LASSO regression analysis for the knee joint cartilage thickness all failed to meet the expectations(R2<0.3,RMSE>0.03).The predictive effect of KNN model on the cartilage thickness of the distal femur lateral condyle and lateral tibial plateau was not ideal(R2=0.23,RMSE=0.29),while it had potential predictive value(accuracy=0.21,accuracy=0.15).Conclusion The prediction model of knee joint cartilage thickness based on individual parameters has certain scientificity,and the feasibility of KNN model is relatively high.However,due to insufficient sample size and unclear individual parameter weight,the efficiencies of the four established prediction models are not ideal,which fails to provide definite prediction equations.Therefore,the construction scheme of the prediction model still needs to be further optimized.

Objective To determine the knee joint cartilage thickness using different methods and explore the feasibility of mathematical statistical models of dataset for the prediction of cartilage thickness.Methods A total of 304 patients diagnosed as knee osteoarthritis(OA)combined with varus deformity and undergoing unilateral total knee arthroplasty at the Second Affiliated Hospital of Army Medical University from March 2023 to March 2024 were selected for the study.All patients had complete preoperative and postoperative clinical data.The healthy cartilage at four anatomical sites of patients,including the distal femur lateral condyle,lateral tibial plateau,posterior medial femoral condyle,and posterior lateral femoral condyle were selected,and the knee joint cartilage thickness was determined based on preoperative MRI analysis,robotic navigation system tracing,tissue section of surgical specimen and digital vernier caliper.The baseline indicators of demographics,disease and imaging ffor patients were collected to construct a dataset,and four models of linear regression analysis,principal component analysis,Least Absolute Shrinkage and Selection Operator(LASSO)regression analysis,and K-nearest neighbors(KNN)analysis were established for predicting the accuracy,determination coefficient(R2)and root mean square error(RMSE),and the regression equation for predicting cartilage thickness was established.Results The knee joint cartilage thicknesses determined by preoperative MRI analysis,robotic navigation system tracing,tissue section of surgical specimen had no statistically significant difference with that by digital vernier caliper(P>0.05).The predictive efficiencies of models of linear regression analysis,principal component analysis,and LASSO regression analysis for the knee joint cartilage thickness all failed to meet the expectations(R2<0.3,RMSE>0.03).The predictive effect of KNN model on the cartilage thickness of the distal femur lateral condyle and lateral tibial plateau was not ideal(R2=0.23,RMSE=0.29),while it had potential predictive value(accuracy=0.21,accuracy=0.15).Conclusion The prediction model of knee joint cartilage thickness based on individual parameters has certain scientificity,and the feasibility of KNN model is relatively high.However,due to insufficient sample size and unclear individual parameter weight,the efficiencies of the four established prediction models are not ideal,which fails to provide definite prediction equations.Therefore,the construction scheme of the prediction model still needs to be further optimized.

Due to the limitations of current anti-HBV therapies, the HBV core (HBc or HBcAg) protein assembly modulators (CpAMs) are believed to be potential anti-HBV agents. Therefore, discovering safe and efficient CpAMs is of great value. In this study, we established a HiBiT-based high-throughput screening system targeting HBc and screened novel CpAMs from an in-house marine chemicals library. A novel lead compound 8a, a derivative of the marine natural product naamidine J, has been successfully screened for potential anti-HBV activity. Bioactivity-driven synthesis was then conducted, and the structure‒activity relationship was analyzed, resulting in the discovery of the most effective compound 11a (IC₅₀ = 0.24 μmol/L). Furthermore, 11a was found to significantly inhibit HBV replication in multiple cell models and exhibit a synergistic effect with tenofovir disoproxil fumarate (TDF) and IFNa2 in vitro for anti-HBV activity. Treatment with 11a in a hydrodynamic-injection mouse model demonstrated significant anti-HBV activity without apparent hepatotoxicity. These findings suggest that the naamidine J derivative 11a could be used as the HBV core protein assembly modulator to develop safe and effective anti-HBV therapies.

As artificial intelligence technology rapidly advances, its deployment within the medical sector presents substantial ethical challenges. Consequently, it becomes crucial to create a standardized, transparent, and secure framework for processing medical data. This includes setting the ethical boundaries for medical artificial intelligence and safeguarding both patient rights and data integrity. This consensus governs every facet of medical data handling through artificial intelligence, encompassing data gathering, processing, storage, transmission, utilization, and sharing. Its purpose is to ensure the management of medical data adheres to ethical standards and legal requirements, while safeguarding patient privacy and data security. Concurrently, the principles of compliance with the law, patient privacy respect, patient interest protection, and safety and reliability are underscored. Key issues such as informed consent, data usage, intellectual property protection, conflict of interest, and benefit sharing are examined in depth. The enactment of this expert consensus is intended to foster the profound integration and sustainable advancement of artificial intelligence within the medical domain, while simultaneously ensuring that artificial intelligence adheres strictly to the relevant ethical norms and legal frameworks during the processing of medical data.
Artificial Intelligence/legislation & jurisprudence* ; Humans ; Consensus ; Computer Security/standards* ; Confidentiality/ethics* ; Informed Consent/ethics*

Background: and Purpose Intravenous tenecteplase (TNK) efficacy has not been well demonstrated in acute ischemic stroke (AIS) beyond 4.5 hours after onset. This study aimed to determine the effect of intravenous TNK for AIS within 4.5 to 24 hours of onset. Methods: In this pilot trial, eligible AIS patients with diffusion-weighted imaging (DWI)-fluid attenuated inversion recovery (FLAIR) mismatch were randomly allocated to intravenous TNK (0.25 mg/kg) or standard care within 4.5–24 hours of onset. The primary endpoint was excellent functional outcome at 90 days (modified Rankin Scale [mRS] score of 0–1). The primary safety endpoint was symptomatic intracranial hemorrhage (sICH). Results: Of the randomly assigned 80 patients, the primary endpoint occurred in 52.5% (21/40) of TNK group and 50.0% (20/40) of control group, with no significant difference (unadjusted odds ratio, 1.11; 95% confidence interval 0.46–2.66; P=0.82). More early neurological improvement occurred in TNK group than in control group (11 vs. 3, P=0.03), but no significant differences were found in other secondary endpoints, such as mRS 0–2 at 90 days, shift analysis of mRS at 90 days, and change in National Institutes of Health Stroke Scale score at 24 hours and 7 days. There were no cases of sICH in this trial; however, asymptomatic intracranial hemorrhage occurred in 3 of the 40 patients (7.5%) in the TNK group. Conclusion This phase 2, randomized, multicenter study suggests that intravenous TNK within 4.5–24 hours of onset may be safe and feasible in AIS patients with a DWI-FLAIR mismatch.

The main components and working principle of the fluid infusion and blood transfusion warmer were introduced,and the causes for the adverse events of the warmer were summarized based on National Medical Device Adverse Event Monitoring Information System and related literature in the world.The potential risks of the warmer were analyzed during operation,and the quality concerns and corresponding evaluation methods were proposed for the warmer from the aspects of device marking and documentation,structure,and temperature.References were provided for standard preparation,pre-market technical review and system verification of the fluid infusion and blood transfusion warmer.[Chinese Medical Equipment Journal,2023,44(10):76-80]

China ; Manganese/toxicity* ; Stainless Steel

There are 200-500 species of Potentilla(Rosaceae) worldwide, among which 90 species are widely distributed in China and have a long history of ethnic medicinal use. According to our statistics, a total of 367 compounds have been isolated and identified from plants of this genus, including terpenoids, flavonoids, phenolic acids, tannins, and phenylpropanoids. The medicinal materials made from these plants mainly have antioxidative, blood sugar-lowering, anti-inflammatory, anti-tumor, cardiovascular system-protecting, neuroprotective, and hepatoprotective activities. This study systematically reviews the research progress on chemical constituents and pharmacological activities of Potentilla plants to provide a basis for further research and clinical application.
Anti-Inflammatory Agents/pharmacology* ; Antioxidants/pharmacology* ; Drugs, Chinese Herbal/pharmacology* ; Plant Extracts/pharmacology* ; Potentilla

Objective: To investigate the expression of programmed death ligand-1 (PD-L1, SP142) and PD-L1 (22C3) in triple-negative breast cancer (TNBC), and analyze their correlation with the clinicopathological factors and prognosis. Methods: The clinicopathologic data of 259 patients with TNBC treated in Cancer Hospital from August 2010 to December 2013 were collected. Whole section of surgical tissue samples were collected to conduct PD-L1 (SP142) and PD-L1 (22C3) immunohistochemical (IHC) staining. The PD-L1 expression in tumor cells and tumor infiltrating immune cells were visually assessed respectively, the relationship between PD-L1 expression and clinicopathologic characterizes were analyzed. Univariable and multivariable Cox proportional hazards regression models were used to test the correlations between PD-L1 expression and disease-free survival (DFS) and overall survival (OS). Results: The positive rates of SP142 (immune cell score, ICs≥1%) and 22C3 (combined positive score, CPS≥1) were 42.1%(109/259) and 41.3%(107/259) in TNBC tissues, respectively, with a total coincidence rate of 82.3%. The Kappa value of positive expression cases was 0.571 and the distribution difference of SP142 and 22C3 positive expression cases was statistically significant (P<0.001). The PD-L1 positive patients were less likely to have vascular invasion (P<0.05), but with higher histological grade and Ki-67 proliferation index (P<0.05). The recurrence/metastasis cases(8) of the patients with positive PD-L1 (SP142) was significantly lower than that of patients with negative PD-L1(SP142, 27, P=0.016). The positive expression of PD-L1 (SP142) patients were longer DFS (P=0.019). The OS of patients with positive PD-L1 (SP142) were longer than those with negative PD-L1 (SP142), but without significance (P=0.116). The positive expression of PD-L1 (22C3) was marginally associated with DFS and OS of patients (P>0.05). Conclusions: The expression of PD-L1 (22C3) is different from that of PD-L1 (SP142) in TNBC, and the two antibodies can't be interchangeable for each other in clinical tests. PD-L1 (SP142) status is an independent prognostic factor of DFS in TNBC. The DFS is significantly prolonged in patients with positive expression of PD-L1 (SP142).
B7-H1 Antigen/genetics* ; Humans ; Immunohistochemistry ; Prognosis ; Triple Negative Breast Neoplasms/pathology*

Objective:To analyze the role of preoperative circulating tumor cell(CTC) and circulating tumor vascular endothelial cells (CTEC) in the diagnosis of gastric cancer and its correlation with the clinicopathological characteristics of gastric cancer.Methods:Sixty-two gastric cancer patients and 11 patients of benign gastric diseases were enrolled. Subtraction enrichment (SE) and immunofluorescence staining-chromosome fluorescence in situ hybridization (i·FISH) were used to integrate the unique SE-i ·FISH technology platform detecting patients′ CTC and CTEC.Results:The number of CTC in the gastric cancer group was significantly higher than that in the control group ( t=2.693, P=0.009); the number of CTEC in the gastric cancer group was higher than the control group ( t=2.015, P=0.048). With the cut-off value being set at 9 cells/6 ml in blood, the sensitivity of CTC in the diagnosis of gastric cancer is 84%, and the specificity is 82% (AUC=0.876, 95% CI, 0.792-0.963, P<0.01); When set at 6 cells/6 ml, the sensitivity of CTEC in the diagnosis of gastric cancer is 50%, and the specificity is 100%(AUC=0.727, 95% CI, 0.603-0.851, P=0.02). CTC positive is closely related to tumor location(χ 2=4.292, P=0.038 ) and TNM stage(CTC≥10, χ 2=4.848, P=0.028; CTC≥11, χ 2=6.234, P=0.013). CTEC positive is closely related to serum CA19-9(χ 2=4.858, P=0.028) and serum CA724 (χ 2=4.108, P=0.043 ) . Conclusion:SE-i·FISH technology has high sensitivity and specificity in the detection of CTC and CTEC of gastric cancer.